321 - What is Particle Swarm Optimization PSO?

Code link: github.com/bns...
Particle Swarm Optimization
PSO is a swarm intelligence algorithm that is inspired by the behavior of social organisms such as flocks of birds or schools of fish.
The algorithm creates a population of particles, each representing a candidate solution, that move through the search space based on their individual velocity and the collective influence of the best solutions found by the swarm.
The algorithm updates the particles' positions and velocities based on the fitness of the current solution and the local and global best solutions found so far. It aims to balance exploration and exploitation by encouraging particles to explore new regions of the search space while also following promising solutions.
PSO is suitable for solving nonlinear and dynamic optimization problems, such as in control systems, machine learning, and signal processing.
PSO has been used for feature selection, image segmentation, and classification in microscopy images. For example, it has been used to optimize the parameters of texture descriptors for image segmentation, and to select the most discriminative features for cell classification.

[@1kerShen]

Thank you for your work, I am from China, I learned image segmentation from your video and successfully finished my graduation project, you are a legend,

[@arungopinath1998]

Another day, another great content.

[@DigitalSreeni]

Thanks again!

[@mohammadyahya78]

amazing. Can we have videos about vision transformers in tensorflow please?

[@JovitaLewis]

could you please illustrate Artificial bee colony(ABC) in some real application?

[@tilkesh]

Thanks

[@DigitalSreeni]

Welcome

[@3elmashy522]

Could you please illustrate Surrogate optimization?

[@DigitalSreeni]

Sure, I will try.

[@3elmashy522]

@@DigitalSreeni Thanks

[@allanjobs3595]

need video for yolov10